SuperDock: A Deep Learning-Based Automated Floating Trash Monitoring System

• Published in: 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO) pp. 1035 - 1040
• Main Authors: Niu, Guanchong, Li, Jie, Guo, Sheng, Pun, Man-On, Hou, Leo, Yang, Lujian
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2019
• Subjects: Autonomous aerial vehicles; Batteries; Deep learning; Docking stations; Floating Trash Monitoring; Monitoring; Real-time systems; Rivers; Simulation; SuperDock; Transfer learning; UAV; YOLO; YOLOv3
• DOI: 10.1109/ROBIO49542.2019.8961509

Floating trash in the river presents a major environmental problem that endangers the lives of river inhabitants. However, conventional trash monitoring relies on labor-intensive manual inspection by dispatching inspectors to the filed, which is highly cost-ineffective. Despite that Unmanned Aerial Vehicles (UAVs) have been widely proposed for many real-time monitoring applications, limited power supply remains the most challenging bottleneck for such UAV-assisted monitoring applications. In this work, we propose an automated river trash monitoring system called SuperDock. SuperDock consists of a remote processing unit, a docking station and a UAV. SuperDock lands the UAV precisely onto the docking station followed by performing automated battery replacement. As a result, the UAV can continue the monitoring task after the battery replacement. Furthermore, SuperDock includes a deep learning-assisted river trash detection module based on YOLOv3 that runs much faster than the conventional Convolution Neural Networks (CNN). In addition, a data set has been generated for training and testing the deep learning network, specifically for the floating trash detection application. Finally, SuperDock enables the UAV to communicate wirelessly with a remote computer in a real-time manner. Experimental and simulation results show that SuperDock is highly effective in monitoring the floating trash in the river.